Abstract
This article examines strategy choices for how people find faults in a simple device by using models of several strategies and new data. Diag, a model solving this task, used a single strategy that predicted the behavior of most participants in a previous study with remarkable accuracy. This article explores additional strategies used in this reasoning task that arise when less directive instructions are provided. Based on our observations, five new strategies for the task were identified and described by being modeled. These different strategies, realized in different models, predict the speed of solution while the participant is learning the task, and were validated by comparing their predictions to the observations (r2 =.27 to.90). The results suggest that participants not only created different strategies for this simple fault-finding task but that some also, with practice, shifted between strategies. This research provides insights into how strategies are an important aspect of the variability in learning, illustrates the transfer of learning on a problem-by-problem level, and shows that the noisiness that most learning curves show can arise from differential transfer between problems.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 133-150 |
| Number of pages | 18 |
| Journal | Cognitive Systems Research |
| Volume | 59 |
| DOIs | |
| State | Published - Jan 1 2020 |
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All Science Journal Classification (ASJC) codes
- Experimental and Cognitive Psychology
- Cognitive Neuroscience
- Artificial Intelligence
Cite this
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Understanding strategy differences in a fault-finding task. / Friedrich, Maik B.; Ritter, Frank Edward.
In: Cognitive Systems Research, Vol. 59, 01.01.2020, p. 133-150.Research output: Contribution to journal › Article
TY - JOUR
T1 - Understanding strategy differences in a fault-finding task
AU - Friedrich, Maik B.
AU - Ritter, Frank Edward
PY - 2020/1/1
Y1 - 2020/1/1
N2 - This article examines strategy choices for how people find faults in a simple device by using models of several strategies and new data. Diag, a model solving this task, used a single strategy that predicted the behavior of most participants in a previous study with remarkable accuracy. This article explores additional strategies used in this reasoning task that arise when less directive instructions are provided. Based on our observations, five new strategies for the task were identified and described by being modeled. These different strategies, realized in different models, predict the speed of solution while the participant is learning the task, and were validated by comparing their predictions to the observations (r2 =.27 to.90). The results suggest that participants not only created different strategies for this simple fault-finding task but that some also, with practice, shifted between strategies. This research provides insights into how strategies are an important aspect of the variability in learning, illustrates the transfer of learning on a problem-by-problem level, and shows that the noisiness that most learning curves show can arise from differential transfer between problems.
AB - This article examines strategy choices for how people find faults in a simple device by using models of several strategies and new data. Diag, a model solving this task, used a single strategy that predicted the behavior of most participants in a previous study with remarkable accuracy. This article explores additional strategies used in this reasoning task that arise when less directive instructions are provided. Based on our observations, five new strategies for the task were identified and described by being modeled. These different strategies, realized in different models, predict the speed of solution while the participant is learning the task, and were validated by comparing their predictions to the observations (r2 =.27 to.90). The results suggest that participants not only created different strategies for this simple fault-finding task but that some also, with practice, shifted between strategies. This research provides insights into how strategies are an important aspect of the variability in learning, illustrates the transfer of learning on a problem-by-problem level, and shows that the noisiness that most learning curves show can arise from differential transfer between problems.
UR - http://www.scopus.com/inward/record.url?scp=85072861739&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072861739&partnerID=8YFLogxK
U2 - 10.1016/j.cogsys.2019.09.017
DO - 10.1016/j.cogsys.2019.09.017
M3 - Article
AN - SCOPUS:85072861739
VL - 59
SP - 133
EP - 150
JO - Cognitive Systems Research
JF - Cognitive Systems Research
SN - 1389-0417
ER -